chore: initial monorepo scaffold + WDS Phase 1+2 artifacts
- Nx 22.7 monorepo (pnpm 11.1, TypeScript 5.9, Node 24) - apps/api: NestJS 11 (CJS conforme CODING-RULES.md PGD-DB-004) - apps/web: React 19 + Vite 8 (ESM) - libs/shared/api-interface: Zod contract base - Docker Compose dev: Postgres 18, Valkey 8, MinIO, Mailpit - WDS artifacts: - design-artifacts/A-Product-Brief/ (5 docs canônicos + 16 dialogs) - design-artifacts/B-Trigger-Map/ (hub + 4 personas + feature impact) - Stack canon: STACK.md v2.2 + CODING-RULES.md v2.0 + brand.md - AGENTS.md + README.md como entrada para devs/agentes Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# Monitoring Templates
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Templates for monitoring impact and iterating in Phase 8 (Product Evolution).
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---
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## Metrics Tracking Dashboard
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```markdown
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# Metrics Tracking: DD-XXX
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**Release Date:** 2024-12-13
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**Measurement Period:** 2024-12-13 to 2024-12-27
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## Daily Tracking
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| Date | Feature X Usage | Drop-off Rate | Notes |
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| ----- | --------------- | ------------- | ------------- |
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| 12/13 | 18% | 38% | Day 1 |
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| 12/14 | 22% | 35% | Trending up |
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| 12/15 | 28% | 30% | Good progress |
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| ... | ... | ... | ... |
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| 12/27 | 58% | 12% | Final |
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## Trend Analysis
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[Chart or description of trends]
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```
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---
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## Qualitative Feedback Tracking
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```markdown
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# Qualitative Feedback: DD-XXX
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## Positive Feedback (8 mentions)
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- "Now I understand how to use Feature X!" (3)
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- "The guide was really helpful" (2)
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- "Love the new onboarding" (3)
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## Negative Feedback (2 mentions)
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- "Guide is too long" (1)
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- "Can't skip the guide" (1)
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## Neutral Feedback (3 mentions)
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- "Didn't notice the change" (3)
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```
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---
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## Impact Report Template
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**File:** `analytics/DD-XXX-impact-report.md`
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```markdown
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# Impact Report: DD-XXX [Name]
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**Release Date:** 2024-12-13
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**Measurement Period:** 2024-12-13 to 2024-12-27
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**Report Date:** 2024-12-28
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---
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## Executive Summary
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**Result:** [SUCCESS | PARTIAL SUCCESS | FAILURE]
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[2-3 sentences summarizing the impact]
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Example:
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"Design Delivery DD-XXX successfully improved Feature X usage from
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15% to 58%, nearly meeting the 60% target. Drop-off decreased
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from 40% to 12%, exceeding the 10% target. User feedback is
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overwhelmingly positive."
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---
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## Metrics Results
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### Metric 1: Feature X Usage Rate
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- **Baseline:** 15%
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- **Target:** 60%
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- **Actual:** 58%
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- **Result:** 97% of target ✅ (PASS)
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- **Trend:** Steady increase over 2 weeks
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### Metric 2: Drop-off Rate
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- **Baseline:** 40%
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- **Target:** 10%
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- **Actual:** 12%
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- **Result:** Exceeded target ✅ (PASS)
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- **Trend:** Sharp decrease in first week, stabilized
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### Metric 3: Support Tickets
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- **Baseline:** 12/month
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- **Target:** 2/month
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- **Actual:** 3/month
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- **Result:** 75% reduction ✅ (PASS)
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### Metric 4: User Satisfaction
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- **Baseline:** 3.2/5
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- **Target:** 4.5/5
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- **Actual:** 4.3/5
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- **Result:** 96% of target ✅ (PASS)
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---
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## Overall Assessment
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**Success Criteria:**
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- Feature X usage > 50% ✅
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- Drop-off < 15% ✅
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- Support tickets < 5/month ✅
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**Result:** SUCCESS ✅
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All success criteria met or exceeded.
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---
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## What Worked
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1. **Inline onboarding was effective**
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- Users understood Feature X immediately
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- Completion rate increased significantly
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2. **Step-by-step guide was helpful**
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- User feedback praised the guide
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- Reduced confusion
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3. **Success celebration was motivating**
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- Users felt accomplished
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- Positive sentiment increased
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---
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## What Didn't Work
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1. **Guide length**
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- Some users found it too long
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- Consider shortening in future iteration
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2. **Skip option**
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- Some users wanted to skip
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- Consider adding "Skip" button
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---
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## Learnings
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1. **Onboarding matters for complex features**
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- Even simple features benefit from guidance
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- First impression is critical
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2. **Measurement validates hypotheses**
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- Our hypothesis was correct
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- Data-driven decisions work
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3. **Small changes have big impact**
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- 3-day effort → 4x usage increase
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- Kaizen philosophy validated
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---
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## Recommendations
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### Short-term (Next Sprint)
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1. Add "Skip" button to guide
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2. Shorten guide from 5 steps to 3 steps
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3. A/B test guide length
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### Long-term (Next Quarter)
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1. Apply onboarding pattern to other features
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2. Create reusable onboarding component
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3. Measure onboarding impact across product
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---
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## Next Kaizen Cycle
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**Based on this success, next improvement opportunity:**
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[Identify next improvement based on learnings]
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Example:
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"Feature Y has similar low usage (20%). Apply same onboarding
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pattern to Feature Y in next Kaizen cycle."
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---
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## Conclusion
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Design Delivery DD-XXX successfully achieved its goals. The
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improvement demonstrates the power of Kaizen - small, focused
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changes that compound over time.
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**Status:** ✅ SUCCESS - Ready for next cycle!
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```
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---
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## Team Results Communication
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```
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WDS Designer → Team
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Subject: Impact Report: DD-XXX - SUCCESS ✅
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Hi Team!
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Impact report for DD-XXX is complete!
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🎉 **Result:** SUCCESS
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📊 **Key Results:**
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- Feature X usage: 15% → 58% (4x increase!)
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- Drop-off: 40% → 12% (70% reduction!)
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- Support tickets: 12/month → 3/month (75% reduction!)
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- User satisfaction: 3.2/5 → 4.3/5
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💡 **Key Learning:**
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Small, focused improvements (3 days effort) can have massive
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impact (4x usage increase). Kaizen philosophy works!
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📁 **Full Report:**
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analytics/DD-XXX-impact-report.md
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🔄 **Next Cycle:**
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Apply same pattern to Feature Y (similar low usage issue).
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Thanks for the great collaboration!
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[Your name]
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WDS Designer
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```
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---
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## Kaizen Cycle Log Template
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```markdown
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# Kaizen Cycle Log
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## Cycle 1: DD-001 Feature X Onboarding
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- Started: 2024-12-09
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- Completed: 2024-12-28
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- Result: SUCCESS ✅
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- Impact: 4x usage increase
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- Learning: Onboarding matters for complex features
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## Cycle 2: DD-002 Feature Y Onboarding
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- Started: 2024-12-28
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- Status: In Progress
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- Goal: Apply validated pattern to similar feature
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- Expected: 4x usage increase
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```
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---
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## Kaizen Prioritization Template
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```markdown
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# Kaizen Prioritization
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## Option A: Refine DD-XXX
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- Impact: Medium (58% → 65%)
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- Effort: Low (1 day)
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- Learning: Low (incremental)
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- Priority: MEDIUM
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## Option B: Apply to Feature Y
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- Impact: High (20% → 80%)
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- Effort: Low (2 days)
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- Learning: High (validates pattern)
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- Priority: HIGH ✅
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## Option C: Fix Feature Z Performance
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- Impact: Medium (35% → 20% drop-off)
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- Effort: Low (1 day)
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- Learning: Medium (performance optimization)
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- Priority: MEDIUM
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**Decision:** Start with Option B (highest priority)
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```
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---
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## Learnings Documentation Template
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```markdown
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# Learnings from DD-XXX
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## What Worked
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1. [Learning 1]
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2. [Learning 2]
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3. [Learning 3]
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## What Didn't Work
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1. [Learning 1]
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2. [Learning 2]
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## Patterns Emerging
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1. [Pattern 1]
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2. [Pattern 2]
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## Hypotheses Validated
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1. [Hypothesis 1]: ✅ Confirmed
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2. [Hypothesis 2]: ❌ Rejected
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## New Questions
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1. [Question 1]
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2. [Question 2]
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```
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---
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## Next Iteration Templates
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### Iterate on Current Update
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```markdown
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# Next Iteration: DD-XXX Refinement
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**Current Status:**
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- Feature X usage: 58% (target: 60%)
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- User feedback: "Guide too long"
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**Next Improvement:**
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- Shorten guide from 5 steps to 3 steps
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- Add "Skip" button
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- A/B test guide length
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**Expected Impact:**
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- Feature X usage: 58% → 65%
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- User satisfaction: 4.3/5 → 4.7/5
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**Effort:** 1 day
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**Priority:** Medium
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```
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### Apply Pattern to Similar Feature
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```markdown
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# Next Opportunity: Apply Pattern to Feature Y
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**Learning from DD-XXX:**
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"Onboarding increases usage 4x for complex features"
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**Similar Problem:**
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- Feature Y usage: 20% (low)
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- User feedback: "Don't understand Feature Y"
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- Similar complexity to Feature X
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**Proposed Solution:**
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Apply same onboarding pattern to Feature Y
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**Expected Impact:**
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- Feature Y usage: 20% → 80% (4x increase)
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- Based on DD-XXX results
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**Effort:** 2 days
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**Priority:** High
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```
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### Address New Problem
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```markdown
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# Next Opportunity: New Problem Identified
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**New Data:**
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- Feature Z drop-off: 35% (increased from 20%)
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- User feedback: "Feature Z is slow"
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- Analytics: Load time 5 seconds (was 2 seconds)
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**Root Cause:**
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Recent update added heavy images, slowing load time
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**Proposed Solution:**
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Optimize images and implement lazy loading
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**Expected Impact:**
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- Load time: 5s → 2s
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- Drop-off: 35% → 20%
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**Effort:** 1 day
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**Priority:** High
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```
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